Applying machine learning to gait analysis data for disease identification

Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Joyseeree, Ranveer (ETH, Zurich, Switzerland) ; Sabha, Rami Abou (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

A machine-learning framework to identify the specific disease afflicting certain patients using only gait analysis data is presented. Classifying such data into disease types consumes valuable clinical time that may be better spent. Effective classification also facilitates its future retrieval. To prove the feasibility of the approach, we applied it to the simpler case of identifying the disease class of patients with a view to extending the method to specific diseases in future work. The patients benefiting from this framework suffer from Neurological and Neuromuscular Diseases (NND), or Juvenile Idiopathic Arthritis (JIA). Standard clinical gait information of healthy individuals, and NND/JIA patients was sourced from hospitals participating in MD-PAEDIGREE. To classify the data into one of the three categories: healthy, NND, and JIA, certain parameters were carefully selected from them and used to train Random Forest (RF), boosting, Multilayer Perceptron (MLP), and Support Vector Machine (SVM) classifiers. Cross-validation was used to test the effectiveness of our approach and it yields a classification accuracy of 100% for RF, SVM, and MLP classifiers and 96.4% for boosting. Training and testing for all the classifiers took mere milliseconds, providing opportunities for real-time applications. To extend the method to the identification of specific illnesses, more discerning features from the gait data are currently being investigated. Moreover, a larger dataset is being gathered. Finally, we are attempting to reduce the number of features used for classification in order to further decrease computation time and algorithm complexity.


Mots-clés:
Type de conférence:
full paper
Faculté:
Economie et Services
Ecole:
HEG VS HES-SO Valais-Wallis - Haute Ecole de Gestion & Tourisme
Institut:
Institut Informatique de gestion
Classification:
Informatique
Autres
Adresse bibliogr.:
Madrid, Spain, 27-29 May 2015
Date:
Madrid, Spain
27-29 May 2015
2015
Pagination:
5 p.
Titre du document hôte:
Proocedings of the Medical Informatics Europe (MIE) 2015
ISSN:
978-1-61499-511-1
Le document apparaît dans:

Note  Le statut de ce document est: non diffusé

Note: The status of this file is: restricted


 Notice créée le 2015-11-20, modifiée le 2018-02-15

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